Last active
December 23, 2017 20:00
-
-
Save sriharsha0806/21ff0c00a83468618c14219a8d8aee79 to your computer and use it in GitHub Desktop.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| name: "segnet" | |
| input:"data" | |
| input_dim: 1 | |
| input_dim: 3 | |
| input_dim: 224 | |
| input_dim: 224 | |
| layer { | |
| name: "norm" | |
| type: "LRN" | |
| bottom: "data" | |
| top: "norm" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "norm" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "conv1" | |
| name: "conv1_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1" | |
| top: "pool1" | |
| top: "pool1_mask" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2" | |
| top: "conv2" | |
| name: "conv2_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2" | |
| type: "ReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "conv2" | |
| top: "pool2" | |
| top: "pool2_mask" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3" | |
| top: "conv3" | |
| name: "conv3_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3" | |
| type: "ReLU" | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "conv3" | |
| top: "pool3" | |
| top: "pool3_mask" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4" | |
| type: "Convolution" | |
| bottom: "pool3" | |
| top: "conv4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4" | |
| top: "conv4" | |
| name: "conv4_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4" | |
| type: "ReLU" | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layer { | |
| name: "pool4" | |
| type: "Pooling" | |
| bottom: "conv4" | |
| top: "pool4" | |
| top: "pool4_mask" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "upsample4" | |
| type: "Upsample" | |
| bottom: "pool4" | |
| bottom: "pool4_mask" | |
| top: "upsample4" | |
| upsample_param { | |
| scale: 2 | |
| pad_out_h: true | |
| } | |
| } | |
| layer { | |
| name: "conv_decode4" | |
| type: "Convolution" | |
| bottom: "upsample4" | |
| top: "conv_decode4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv_decode4" | |
| top: "conv_decode4" | |
| name: "conv_decode4_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "upsample3" | |
| type: "Upsample" | |
| bottom: "conv_decode4" | |
| bottom: "pool3_mask" | |
| top: "upsample3" | |
| upsample_param { | |
| scale: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv_decode3" | |
| type: "Convolution" | |
| bottom: "upsample3" | |
| top: "conv_decode3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv_decode3" | |
| top: "conv_decode3" | |
| name: "conv_decode3_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "upsample2" | |
| type: "Upsample" | |
| bottom: "conv_decode3" | |
| bottom: "pool2_mask" | |
| top: "upsample2" | |
| upsample_param { | |
| scale: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv_decode2" | |
| type: "Convolution" | |
| bottom: "upsample2" | |
| top: "conv_decode2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv_decode2" | |
| top: "conv_decode2" | |
| name: "conv_decode2_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "upsample1" | |
| type: "Upsample" | |
| bottom: "conv_decode2" | |
| bottom: "pool1_mask" | |
| top: "upsample1" | |
| upsample_param { | |
| scale: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv_decode1" | |
| type: "Convolution" | |
| bottom: "upsample1" | |
| top: "conv_decode1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv_decode1" | |
| top: "conv_decode1" | |
| name: "conv_decode1_bn" | |
| type: "BN" | |
| bn_param { | |
| bn_mode: INFERENCE | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv_classifier" | |
| type: "Convolution" | |
| bottom: "conv_decode1" | |
| top: "conv_classifier" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 11 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "prob" | |
| type: "Softmax" | |
| bottom: "conv_classifier" | |
| top: "prob" | |
| softmax_param {engine: CAFFE} | |
| } |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment